


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ImageGPTImageProcessor







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case=7   ,    snake_case=3   ,    snake_case=18   ,    snake_case=30   ,    snake_case=400   ,    snake_case=True   ,    snake_case=None   ,    snake_case=True   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							size if size is not None else {"height": 18, "width": 18}
						UpperCamelCase__       =							parent
						UpperCamelCase__       =							batch_size
						UpperCamelCase__       =							num_channels
						UpperCamelCase__       =							image_size
						UpperCamelCase__       =							min_resolution
						UpperCamelCase__       =							max_resolution
						UpperCamelCase__       =							do_resize
						UpperCamelCase__       =							size
						UpperCamelCase__       =							do_normalize







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return {
						    # here we create 2 clusters for the sake of simplicity
						    "clusters": np.asarray(
						        [
						            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
						            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
						        ]  ),
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						}







@require_torch
@require_vision
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		ImageGPTImageProcessor if is_vision_available() else None




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							ImageGPTImageProcessingTester(self  )




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(snake_case   ,    "clusters"  )  )
						self.assertTrue(hasattr(snake_case   ,    "do_resize"  )  )
						self.assertTrue(hasattr(snake_case   ,    "size"  )  )
						self.assertTrue(hasattr(snake_case   ,    "do_normalize"  )  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class.from_dict(self.image_processor_dict  )
						self.assertEqual(image_processor.size   ,    {"height": 18, "width": 18}  )

						UpperCamelCase__       =							self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42  )
						self.assertEqual(image_processor.size   ,    {"height": 42, "width": 42}  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						UpperCamelCase__       =							json.loads(image_processor.to_json_string()  )
						for key, value in self.image_processor_dict.items():
									if key == "clusters":
												self.assertTrue(np.array_equal(snake_case   ,    obj[key]  )  )
									else:
												self.assertEqual(obj[key]   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )

						with tempfile.TemporaryDirectory() as tmpdirname:
									UpperCamelCase__       =							os.path.join(snake_case   ,    "image_processor.json"  )
									image_processor_first.to_json_file(snake_case  )
									UpperCamelCase__       =							self.image_processing_class.from_json_file(snake_case  ).to_dict()

						UpperCamelCase__       =							image_processor_first.to_dict()
						for key, value in image_processor_first.items():
									if key == "clusters":
												self.assertTrue(np.array_equal(snake_case   ,    image_processor_second[key]  )  )
									else:
												self.assertEqual(image_processor_first[key]   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )

						with tempfile.TemporaryDirectory() as tmpdirname:
									image_processor_first.save_pretrained(snake_case  )
									UpperCamelCase__       =							self.image_processing_class.from_pretrained(snake_case  ).to_dict()

						UpperCamelCase__       =							image_processor_first.to_dict()
						for key, value in image_processor_first.items():
									if key == "clusters":
												self.assertTrue(np.array_equal(snake_case   ,    image_processor_second[key]  )  )
									else:
												self.assertEqual(image_processor_first[key]   ,    snake_case  )







			@unittest.skip("ImageGPT requires clusters at initialization"  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass



def 							UpperCamelCase_(					)->		List[Any]:
			UpperCamelCase__       =							load_dataset("hf-internal-testing/fixtures_image_utils"					,  split="test"	)

			UpperCamelCase__       =							Image.open(dataset[4]["file"]	)
			UpperCamelCase__       =							Image.open(dataset[5]["file"]	)

			UpperCamelCase__       =							[imagea, imagea]

			return images







@require_vision
@require_torch
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"  )

						UpperCamelCase__       =							prepare_images()

						# test non-batched
						UpperCamelCase__       =							image_processing(images[0]   ,    return_tensors="pt"  )

						self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor  )
						self.assertEqual(encoding.input_ids.shape   ,    (1, 1024)  )

						UpperCamelCase__       =							[306, 191, 191]
						self.assertEqual(encoding.input_ids[0, :3].tolist()   ,    snake_case  )

						# test batched
						UpperCamelCase__       =							image_processing(snake_case   ,    return_tensors="pt"  )

						self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor  )
						self.assertEqual(encoding.input_ids.shape   ,    (2, 1024)  )

						UpperCamelCase__       =							[303, 13, 13]
						self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,    snake_case  )






import numpy as np
def 							UpperCamelCase_(					_A :np.array	)->		np.array:
			return 1 / (1 + np.exp(-vector	))


if __name__ == "__main__":
						import doctest

						doctest.testmod()


